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sobamchan
/
st5-base-mean-8000

Sentence Similarity
sentence-transformers
Safetensors
English
t5
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MultipleNegativesRankingLoss
Inference Endpoints
Model card Files Community
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  1. 1_Pooling/config.json +10 -0
  2. README.md +1196 -0
  3. config.json +61 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +20 -0
  7. optimizer.pt +3 -0
  8. rng_state.pth +3 -0
  9. scheduler.pt +3 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +107 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +939 -0
  14. trainer_state.json +0 -0
  15. training_args.bin +3 -0
1_Pooling/config.json ADDED
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1
+ {
2
+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,1196 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - en
4
+ tags:
5
+ - sentence-transformers
6
+ - sentence-similarity
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+ - feature-extraction
8
+ - generated_from_trainer
9
+ - dataset_size:557850
10
+ - loss:MultipleNegativesRankingLoss
11
+ base_model: google-t5/t5-base
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+ widget:
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+ - source_sentence: A man is jumping unto his filthy bed.
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+ sentences:
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+ - A young male is looking at a newspaper while 2 females walks past him.
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+ - The bed is dirty.
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+ - The man is on the moon.
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+ - source_sentence: A carefully balanced male stands on one foot near a clean ocean
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+ beach area.
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+ sentences:
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+ - A man is ouside near the beach.
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+ - Three policemen patrol the streets on bikes
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+ - A man is sitting on his couch.
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+ - source_sentence: The man is wearing a blue shirt.
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+ sentences:
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+ - Near the trashcan the man stood and smoked
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+ - A man in a blue shirt leans on a wall beside a road with a blue van and red car
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+ with water in the background.
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+ - A man in a black shirt is playing a guitar.
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+ - source_sentence: The girls are outdoors.
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+ sentences:
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+ - Two girls riding on an amusement part ride.
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+ - a guy laughs while doing laundry
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+ - Three girls are standing together in a room, one is listening, one is writing
35
+ on a wall and the third is talking to them.
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+ - source_sentence: A construction worker peeking out of a manhole while his coworker
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+ sits on the sidewalk smiling.
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+ sentences:
39
+ - A worker is looking out of a manhole.
40
+ - A man is giving a presentation.
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+ - The workers are both inside the manhole.
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+ datasets:
43
+ - sentence-transformers/all-nli
44
+ pipeline_tag: sentence-similarity
45
+ library_name: sentence-transformers
46
+ ---
47
+
48
+ # SentenceTransformer based on google-t5/t5-base
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+
50
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
54
+ ### Model Description
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+ - **Model Type:** Sentence Transformer
56
+ - **Base model:** [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) <!-- at revision a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 -->
57
+ - **Maximum Sequence Length:** 256 tokens
58
+ - **Output Dimensionality:** 768 dimensions
59
+ - **Similarity Function:** Cosine Similarity
60
+ - **Training Dataset:**
61
+ - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
62
+ - **Language:** en
63
+ <!-- - **License:** Unknown -->
64
+
65
+ ### Model Sources
66
+
67
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
68
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
69
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
71
+ ### Full Model Architecture
72
+
73
+ ```
74
+ SentenceTransformer(
75
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: T5EncoderModel
76
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
77
+ (2): Normalize()
78
+ )
79
+ ```
80
+
81
+ ## Usage
82
+
83
+ ### Direct Usage (Sentence Transformers)
84
+
85
+ First install the Sentence Transformers library:
86
+
87
+ ```bash
88
+ pip install -U sentence-transformers
89
+ ```
90
+
91
+ Then you can load this model and run inference.
92
+ ```python
93
+ from sentence_transformers import SentenceTransformer
94
+
95
+ # Download from the 🤗 Hub
96
+ model = SentenceTransformer("sentence_transformers_model_id")
97
+ # Run inference
98
+ sentences = [
99
+ 'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
100
+ 'A worker is looking out of a manhole.',
101
+ 'The workers are both inside the manhole.',
102
+ ]
103
+ embeddings = model.encode(sentences)
104
+ print(embeddings.shape)
105
+ # [3, 768]
106
+
107
+ # Get the similarity scores for the embeddings
108
+ similarities = model.similarity(embeddings, embeddings)
109
+ print(similarities.shape)
110
+ # [3, 3]
111
+ ```
112
+
113
+ <!--
114
+ ### Direct Usage (Transformers)
115
+
116
+ <details><summary>Click to see the direct usage in Transformers</summary>
117
+
118
+ </details>
119
+ -->
120
+
121
+ <!--
122
+ ### Downstream Usage (Sentence Transformers)
123
+
124
+ You can finetune this model on your own dataset.
125
+
126
+ <details><summary>Click to expand</summary>
127
+
128
+ </details>
129
+ -->
130
+
131
+ <!--
132
+ ### Out-of-Scope Use
133
+
134
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
135
+ -->
136
+
137
+ <!--
138
+ ## Bias, Risks and Limitations
139
+
140
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
141
+ -->
142
+
143
+ <!--
144
+ ### Recommendations
145
+
146
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
147
+ -->
148
+
149
+ ## Training Details
150
+
151
+ ### Training Dataset
152
+
153
+ #### all-nli
154
+
155
+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
156
+ * Size: 557,850 training samples
157
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
158
+ * Approximate statistics based on the first 1000 samples:
159
+ | | anchor | positive | negative |
160
+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 9.96 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.79 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.02 tokens</li><li>max: 57 tokens</li></ul> |
163
+ * Samples:
164
+ | anchor | positive | negative |
165
+ |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
166
+ | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
167
+ | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
168
+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
169
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
170
+ ```json
171
+ {
172
+ "scale": 20.0,
173
+ "similarity_fct": "cos_sim"
174
+ }
175
+ ```
176
+
177
+ ### Evaluation Dataset
178
+
179
+ #### all-nli
180
+
181
+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
182
+ * Size: 6,584 evaluation samples
183
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
184
+ * Approximate statistics based on the first 1000 samples:
185
+ | | anchor | positive | negative |
186
+ |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
187
+ | type | string | string | string |
188
+ | details | <ul><li>min: 5 tokens</li><li>mean: 19.41 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.69 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.35 tokens</li><li>max: 30 tokens</li></ul> |
189
+ * Samples:
190
+ | anchor | positive | negative |
191
+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
192
+ | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
193
+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
194
+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
195
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
196
+ ```json
197
+ {
198
+ "scale": 20.0,
199
+ "similarity_fct": "cos_sim"
200
+ }
201
+ ```
202
+
203
+ ### Training Hyperparameters
204
+ #### Non-Default Hyperparameters
205
+
206
+ - `eval_strategy`: steps
207
+ - `per_device_train_batch_size`: 64
208
+ - `per_device_eval_batch_size`: 64
209
+ - `learning_rate`: 1e-05
210
+ - `warmup_ratio`: 0.1
211
+ - `batch_sampler`: no_duplicates
212
+
213
+ #### All Hyperparameters
214
+ <details><summary>Click to expand</summary>
215
+
216
+ - `overwrite_output_dir`: False
217
+ - `do_predict`: False
218
+ - `eval_strategy`: steps
219
+ - `prediction_loss_only`: True
220
+ - `per_device_train_batch_size`: 64
221
+ - `per_device_eval_batch_size`: 64
222
+ - `per_gpu_train_batch_size`: None
223
+ - `per_gpu_eval_batch_size`: None
224
+ - `gradient_accumulation_steps`: 1
225
+ - `eval_accumulation_steps`: None
226
+ - `torch_empty_cache_steps`: None
227
+ - `learning_rate`: 1e-05
228
+ - `weight_decay`: 0.0
229
+ - `adam_beta1`: 0.9
230
+ - `adam_beta2`: 0.999
231
+ - `adam_epsilon`: 1e-08
232
+ - `max_grad_norm`: 1.0
233
+ - `num_train_epochs`: 3
234
+ - `max_steps`: -1
235
+ - `lr_scheduler_type`: linear
236
+ - `lr_scheduler_kwargs`: {}
237
+ - `warmup_ratio`: 0.1
238
+ - `warmup_steps`: 0
239
+ - `log_level`: passive
240
+ - `log_level_replica`: warning
241
+ - `log_on_each_node`: True
242
+ - `logging_nan_inf_filter`: True
243
+ - `save_safetensors`: True
244
+ - `save_on_each_node`: False
245
+ - `save_only_model`: False
246
+ - `restore_callback_states_from_checkpoint`: False
247
+ - `no_cuda`: False
248
+ - `use_cpu`: False
249
+ - `use_mps_device`: False
250
+ - `seed`: 42
251
+ - `data_seed`: None
252
+ - `jit_mode_eval`: False
253
+ - `use_ipex`: False
254
+ - `bf16`: False
255
+ - `fp16`: False
256
+ - `fp16_opt_level`: O1
257
+ - `half_precision_backend`: auto
258
+ - `bf16_full_eval`: False
259
+ - `fp16_full_eval`: False
260
+ - `tf32`: None
261
+ - `local_rank`: 0
262
+ - `ddp_backend`: None
263
+ - `tpu_num_cores`: None
264
+ - `tpu_metrics_debug`: False
265
+ - `debug`: []
266
+ - `dataloader_drop_last`: False
267
+ - `dataloader_num_workers`: 0
268
+ - `dataloader_prefetch_factor`: None
269
+ - `past_index`: -1
270
+ - `disable_tqdm`: False
271
+ - `remove_unused_columns`: True
272
+ - `label_names`: None
273
+ - `load_best_model_at_end`: False
274
+ - `ignore_data_skip`: False
275
+ - `fsdp`: []
276
+ - `fsdp_min_num_params`: 0
277
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
278
+ - `fsdp_transformer_layer_cls_to_wrap`: None
279
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
280
+ - `deepspeed`: None
281
+ - `label_smoothing_factor`: 0.0
282
+ - `optim`: adamw_torch
283
+ - `optim_args`: None
284
+ - `adafactor`: False
285
+ - `group_by_length`: False
286
+ - `length_column_name`: length
287
+ - `ddp_find_unused_parameters`: None
288
+ - `ddp_bucket_cap_mb`: None
289
+ - `ddp_broadcast_buffers`: False
290
+ - `dataloader_pin_memory`: True
291
+ - `dataloader_persistent_workers`: False
292
+ - `skip_memory_metrics`: True
293
+ - `use_legacy_prediction_loop`: False
294
+ - `push_to_hub`: False
295
+ - `resume_from_checkpoint`: None
296
+ - `hub_model_id`: None
297
+ - `hub_strategy`: every_save
298
+ - `hub_private_repo`: None
299
+ - `hub_always_push`: False
300
+ - `gradient_checkpointing`: False
301
+ - `gradient_checkpointing_kwargs`: None
302
+ - `include_inputs_for_metrics`: False
303
+ - `include_for_metrics`: []
304
+ - `eval_do_concat_batches`: True
305
+ - `fp16_backend`: auto
306
+ - `push_to_hub_model_id`: None
307
+ - `push_to_hub_organization`: None
308
+ - `mp_parameters`:
309
+ - `auto_find_batch_size`: False
310
+ - `full_determinism`: False
311
+ - `torchdynamo`: None
312
+ - `ray_scope`: last
313
+ - `ddp_timeout`: 1800
314
+ - `torch_compile`: False
315
+ - `torch_compile_backend`: None
316
+ - `torch_compile_mode`: None
317
+ - `dispatch_batches`: None
318
+ - `split_batches`: None
319
+ - `include_tokens_per_second`: False
320
+ - `include_num_input_tokens_seen`: False
321
+ - `neftune_noise_alpha`: None
322
+ - `optim_target_modules`: None
323
+ - `batch_eval_metrics`: False
324
+ - `eval_on_start`: False
325
+ - `use_liger_kernel`: False
326
+ - `eval_use_gather_object`: False
327
+ - `average_tokens_across_devices`: False
328
+ - `prompts`: None
329
+ - `batch_sampler`: no_duplicates
330
+ - `multi_dataset_batch_sampler`: proportional
331
+
332
+ </details>
333
+
334
+ ### Training Logs
335
+ <details><summary>Click to expand</summary>
336
+
337
+ | Epoch | Step | Training Loss | Validation Loss |
338
+ |:------:|:----:|:-------------:|:---------------:|
339
+ | 0.0011 | 10 | - | 1.8733 |
340
+ | 0.0023 | 20 | - | 1.8726 |
341
+ | 0.0034 | 30 | - | 1.8714 |
342
+ | 0.0046 | 40 | - | 1.8697 |
343
+ | 0.0057 | 50 | - | 1.8675 |
344
+ | 0.0069 | 60 | - | 1.8649 |
345
+ | 0.0080 | 70 | - | 1.8619 |
346
+ | 0.0092 | 80 | - | 1.8584 |
347
+ | 0.0103 | 90 | - | 1.8544 |
348
+ | 0.0115 | 100 | 3.1046 | 1.8499 |
349
+ | 0.0126 | 110 | - | 1.8451 |
350
+ | 0.0138 | 120 | - | 1.8399 |
351
+ | 0.0149 | 130 | - | 1.8343 |
352
+ | 0.0161 | 140 | - | 1.8283 |
353
+ | 0.0172 | 150 | - | 1.8223 |
354
+ | 0.0184 | 160 | - | 1.8159 |
355
+ | 0.0195 | 170 | - | 1.8091 |
356
+ | 0.0206 | 180 | - | 1.8016 |
357
+ | 0.0218 | 190 | - | 1.7938 |
358
+ | 0.0229 | 200 | 3.0303 | 1.7858 |
359
+ | 0.0241 | 210 | - | 1.7775 |
360
+ | 0.0252 | 220 | - | 1.7693 |
361
+ | 0.0264 | 230 | - | 1.7605 |
362
+ | 0.0275 | 240 | - | 1.7514 |
363
+ | 0.0287 | 250 | - | 1.7417 |
364
+ | 0.0298 | 260 | - | 1.7320 |
365
+ | 0.0310 | 270 | - | 1.7227 |
366
+ | 0.0321 | 280 | - | 1.7134 |
367
+ | 0.0333 | 290 | - | 1.7040 |
368
+ | 0.0344 | 300 | 2.9459 | 1.6941 |
369
+ | 0.0356 | 310 | - | 1.6833 |
370
+ | 0.0367 | 320 | - | 1.6725 |
371
+ | 0.0379 | 330 | - | 1.6614 |
372
+ | 0.0390 | 340 | - | 1.6510 |
373
+ | 0.0402 | 350 | - | 1.6402 |
374
+ | 0.0413 | 360 | - | 1.6296 |
375
+ | 0.0424 | 370 | - | 1.6187 |
376
+ | 0.0436 | 380 | - | 1.6073 |
377
+ | 0.0447 | 390 | - | 1.5962 |
378
+ | 0.0459 | 400 | 2.7813 | 1.5848 |
379
+ | 0.0470 | 410 | - | 1.5735 |
380
+ | 0.0482 | 420 | - | 1.5620 |
381
+ | 0.0493 | 430 | - | 1.5495 |
382
+ | 0.0505 | 440 | - | 1.5375 |
383
+ | 0.0516 | 450 | - | 1.5256 |
384
+ | 0.0528 | 460 | - | 1.5133 |
385
+ | 0.0539 | 470 | - | 1.5012 |
386
+ | 0.0551 | 480 | - | 1.4892 |
387
+ | 0.0562 | 490 | - | 1.4769 |
388
+ | 0.0574 | 500 | 2.6308 | 1.4640 |
389
+ | 0.0585 | 510 | - | 1.4513 |
390
+ | 0.0597 | 520 | - | 1.4391 |
391
+ | 0.0608 | 530 | - | 1.4262 |
392
+ | 0.0619 | 540 | - | 1.4130 |
393
+ | 0.0631 | 550 | - | 1.3998 |
394
+ | 0.0642 | 560 | - | 1.3874 |
395
+ | 0.0654 | 570 | - | 1.3752 |
396
+ | 0.0665 | 580 | - | 1.3620 |
397
+ | 0.0677 | 590 | - | 1.3485 |
398
+ | 0.0688 | 600 | 2.4452 | 1.3350 |
399
+ | 0.0700 | 610 | - | 1.3213 |
400
+ | 0.0711 | 620 | - | 1.3088 |
401
+ | 0.0723 | 630 | - | 1.2965 |
402
+ | 0.0734 | 640 | - | 1.2839 |
403
+ | 0.0746 | 650 | - | 1.2713 |
404
+ | 0.0757 | 660 | - | 1.2592 |
405
+ | 0.0769 | 670 | - | 1.2466 |
406
+ | 0.0780 | 680 | - | 1.2332 |
407
+ | 0.0792 | 690 | - | 1.2203 |
408
+ | 0.0803 | 700 | 2.2626 | 1.2077 |
409
+ | 0.0815 | 710 | - | 1.1959 |
410
+ | 0.0826 | 720 | - | 1.1841 |
411
+ | 0.0837 | 730 | - | 1.1725 |
412
+ | 0.0849 | 740 | - | 1.1619 |
413
+ | 0.0860 | 750 | - | 1.1516 |
414
+ | 0.0872 | 760 | - | 1.1416 |
415
+ | 0.0883 | 770 | - | 1.1320 |
416
+ | 0.0895 | 780 | - | 1.1227 |
417
+ | 0.0906 | 790 | - | 1.1138 |
418
+ | 0.0918 | 800 | 2.0044 | 1.1053 |
419
+ | 0.0929 | 810 | - | 1.0965 |
420
+ | 0.0941 | 820 | - | 1.0879 |
421
+ | 0.0952 | 830 | - | 1.0796 |
422
+ | 0.0964 | 840 | - | 1.0718 |
423
+ | 0.0975 | 850 | - | 1.0644 |
424
+ | 0.0987 | 860 | - | 1.0564 |
425
+ | 0.0998 | 870 | - | 1.0490 |
426
+ | 0.1010 | 880 | - | 1.0417 |
427
+ | 0.1021 | 890 | - | 1.0354 |
428
+ | 0.1032 | 900 | 1.8763 | 1.0296 |
429
+ | 0.1044 | 910 | - | 1.0239 |
430
+ | 0.1055 | 920 | - | 1.0180 |
431
+ | 0.1067 | 930 | - | 1.0123 |
432
+ | 0.1078 | 940 | - | 1.0065 |
433
+ | 0.1090 | 950 | - | 1.0008 |
434
+ | 0.1101 | 960 | - | 0.9950 |
435
+ | 0.1113 | 970 | - | 0.9894 |
436
+ | 0.1124 | 980 | - | 0.9840 |
437
+ | 0.1136 | 990 | - | 0.9793 |
438
+ | 0.1147 | 1000 | 1.7287 | 0.9752 |
439
+ | 0.1159 | 1010 | - | 0.9706 |
440
+ | 0.1170 | 1020 | - | 0.9659 |
441
+ | 0.1182 | 1030 | - | 0.9615 |
442
+ | 0.1193 | 1040 | - | 0.9572 |
443
+ | 0.1205 | 1050 | - | 0.9531 |
444
+ | 0.1216 | 1060 | - | 0.9494 |
445
+ | 0.1227 | 1070 | - | 0.9456 |
446
+ | 0.1239 | 1080 | - | 0.9415 |
447
+ | 0.1250 | 1090 | - | 0.9377 |
448
+ | 0.1262 | 1100 | 1.6312 | 0.9339 |
449
+ | 0.1273 | 1110 | - | 0.9303 |
450
+ | 0.1285 | 1120 | - | 0.9267 |
451
+ | 0.1296 | 1130 | - | 0.9232 |
452
+ | 0.1308 | 1140 | - | 0.9197 |
453
+ | 0.1319 | 1150 | - | 0.9162 |
454
+ | 0.1331 | 1160 | - | 0.9128 |
455
+ | 0.1342 | 1170 | - | 0.9097 |
456
+ | 0.1354 | 1180 | - | 0.9069 |
457
+ | 0.1365 | 1190 | - | 0.9040 |
458
+ | 0.1377 | 1200 | 1.5316 | 0.9010 |
459
+ | 0.1388 | 1210 | - | 0.8979 |
460
+ | 0.1400 | 1220 | - | 0.8947 |
461
+ | 0.1411 | 1230 | - | 0.8915 |
462
+ | 0.1423 | 1240 | - | 0.8888 |
463
+ | 0.1434 | 1250 | - | 0.8861 |
464
+ | 0.1445 | 1260 | - | 0.8833 |
465
+ | 0.1457 | 1270 | - | 0.8806 |
466
+ | 0.1468 | 1280 | - | 0.8779 |
467
+ | 0.1480 | 1290 | - | 0.8748 |
468
+ | 0.1491 | 1300 | 1.4961 | 0.8718 |
469
+ | 0.1503 | 1310 | - | 0.8690 |
470
+ | 0.1514 | 1320 | - | 0.8664 |
471
+ | 0.1526 | 1330 | - | 0.8635 |
472
+ | 0.1537 | 1340 | - | 0.8603 |
473
+ | 0.1549 | 1350 | - | 0.8574 |
474
+ | 0.1560 | 1360 | - | 0.8545 |
475
+ | 0.1572 | 1370 | - | 0.8521 |
476
+ | 0.1583 | 1380 | - | 0.8497 |
477
+ | 0.1595 | 1390 | - | 0.8474 |
478
+ | 0.1606 | 1400 | 1.451 | 0.8453 |
479
+ | 0.1618 | 1410 | - | 0.8429 |
480
+ | 0.1629 | 1420 | - | 0.8404 |
481
+ | 0.1640 | 1430 | - | 0.8380 |
482
+ | 0.1652 | 1440 | - | 0.8357 |
483
+ | 0.1663 | 1450 | - | 0.8336 |
484
+ | 0.1675 | 1460 | - | 0.8312 |
485
+ | 0.1686 | 1470 | - | 0.8289 |
486
+ | 0.1698 | 1480 | - | 0.8262 |
487
+ | 0.1709 | 1490 | - | 0.8236 |
488
+ | 0.1721 | 1500 | 1.4177 | 0.8213 |
489
+ | 0.1732 | 1510 | - | 0.8189 |
490
+ | 0.1744 | 1520 | - | 0.8168 |
491
+ | 0.1755 | 1530 | - | 0.8147 |
492
+ | 0.1767 | 1540 | - | 0.8127 |
493
+ | 0.1778 | 1550 | - | 0.8107 |
494
+ | 0.1790 | 1560 | - | 0.8082 |
495
+ | 0.1801 | 1570 | - | 0.8059 |
496
+ | 0.1813 | 1580 | - | 0.8036 |
497
+ | 0.1824 | 1590 | - | 0.8015 |
498
+ | 0.1835 | 1600 | 1.3734 | 0.7993 |
499
+ | 0.1847 | 1610 | - | 0.7970 |
500
+ | 0.1858 | 1620 | - | 0.7948 |
501
+ | 0.1870 | 1630 | - | 0.7922 |
502
+ | 0.1881 | 1640 | - | 0.7900 |
503
+ | 0.1893 | 1650 | - | 0.7877 |
504
+ | 0.1904 | 1660 | - | 0.7852 |
505
+ | 0.1916 | 1670 | - | 0.7829 |
506
+ | 0.1927 | 1680 | - | 0.7804 |
507
+ | 0.1939 | 1690 | - | 0.7779 |
508
+ | 0.1950 | 1700 | 1.3327 | 0.7757 |
509
+ | 0.1962 | 1710 | - | 0.7738 |
510
+ | 0.1973 | 1720 | - | 0.7719 |
511
+ | 0.1985 | 1730 | - | 0.7700 |
512
+ | 0.1996 | 1740 | - | 0.7679 |
513
+ | 0.2008 | 1750 | - | 0.7658 |
514
+ | 0.2019 | 1760 | - | 0.7641 |
515
+ | 0.2031 | 1770 | - | 0.7621 |
516
+ | 0.2042 | 1780 | - | 0.7601 |
517
+ | 0.2053 | 1790 | - | 0.7580 |
518
+ | 0.2065 | 1800 | 1.2804 | 0.7558 |
519
+ | 0.2076 | 1810 | - | 0.7536 |
520
+ | 0.2088 | 1820 | - | 0.7514 |
521
+ | 0.2099 | 1830 | - | 0.7493 |
522
+ | 0.2111 | 1840 | - | 0.7473 |
523
+ | 0.2122 | 1850 | - | 0.7451 |
524
+ | 0.2134 | 1860 | - | 0.7429 |
525
+ | 0.2145 | 1870 | - | 0.7408 |
526
+ | 0.2157 | 1880 | - | 0.7389 |
527
+ | 0.2168 | 1890 | - | 0.7368 |
528
+ | 0.2180 | 1900 | 1.2255 | 0.7349 |
529
+ | 0.2191 | 1910 | - | 0.7328 |
530
+ | 0.2203 | 1920 | - | 0.7310 |
531
+ | 0.2214 | 1930 | - | 0.7293 |
532
+ | 0.2226 | 1940 | - | 0.7277 |
533
+ | 0.2237 | 1950 | - | 0.7259 |
534
+ | 0.2248 | 1960 | - | 0.7240 |
535
+ | 0.2260 | 1970 | - | 0.7221 |
536
+ | 0.2271 | 1980 | - | 0.7203 |
537
+ | 0.2283 | 1990 | - | 0.7184 |
538
+ | 0.2294 | 2000 | 1.2635 | 0.7165 |
539
+ | 0.2306 | 2010 | - | 0.7150 |
540
+ | 0.2317 | 2020 | - | 0.7135 |
541
+ | 0.2329 | 2030 | - | 0.7117 |
542
+ | 0.2340 | 2040 | - | 0.7099 |
543
+ | 0.2352 | 2050 | - | 0.7084 |
544
+ | 0.2363 | 2060 | - | 0.7068 |
545
+ | 0.2375 | 2070 | - | 0.7054 |
546
+ | 0.2386 | 2080 | - | 0.7037 |
547
+ | 0.2398 | 2090 | - | 0.7023 |
548
+ | 0.2409 | 2100 | 1.1912 | 0.7009 |
549
+ | 0.2421 | 2110 | - | 0.6991 |
550
+ | 0.2432 | 2120 | - | 0.6974 |
551
+ | 0.2444 | 2130 | - | 0.6962 |
552
+ | 0.2455 | 2140 | - | 0.6950 |
553
+ | 0.2466 | 2150 | - | 0.6938 |
554
+ | 0.2478 | 2160 | - | 0.6922 |
555
+ | 0.2489 | 2170 | - | 0.6909 |
556
+ | 0.2501 | 2180 | - | 0.6897 |
557
+ | 0.2512 | 2190 | - | 0.6884 |
558
+ | 0.2524 | 2200 | 1.2144 | 0.6868 |
559
+ | 0.2535 | 2210 | - | 0.6856 |
560
+ | 0.2547 | 2220 | - | 0.6843 |
561
+ | 0.2558 | 2230 | - | 0.6829 |
562
+ | 0.2570 | 2240 | - | 0.6817 |
563
+ | 0.2581 | 2250 | - | 0.6804 |
564
+ | 0.2593 | 2260 | - | 0.6789 |
565
+ | 0.2604 | 2270 | - | 0.6775 |
566
+ | 0.2616 | 2280 | - | 0.6763 |
567
+ | 0.2627 | 2290 | - | 0.6751 |
568
+ | 0.2639 | 2300 | 1.1498 | 0.6739 |
569
+ | 0.2650 | 2310 | - | 0.6725 |
570
+ | 0.2661 | 2320 | - | 0.6711 |
571
+ | 0.2673 | 2330 | - | 0.6698 |
572
+ | 0.2684 | 2340 | - | 0.6684 |
573
+ | 0.2696 | 2350 | - | 0.6666 |
574
+ | 0.2707 | 2360 | - | 0.6653 |
575
+ | 0.2719 | 2370 | - | 0.6638 |
576
+ | 0.2730 | 2380 | - | 0.6621 |
577
+ | 0.2742 | 2390 | - | 0.6609 |
578
+ | 0.2753 | 2400 | 1.1446 | 0.6596 |
579
+ | 0.2765 | 2410 | - | 0.6582 |
580
+ | 0.2776 | 2420 | - | 0.6568 |
581
+ | 0.2788 | 2430 | - | 0.6553 |
582
+ | 0.2799 | 2440 | - | 0.6541 |
583
+ | 0.2811 | 2450 | - | 0.6527 |
584
+ | 0.2822 | 2460 | - | 0.6513 |
585
+ | 0.2834 | 2470 | - | 0.6496 |
586
+ | 0.2845 | 2480 | - | 0.6483 |
587
+ | 0.2856 | 2490 | - | 0.6475 |
588
+ | 0.2868 | 2500 | 1.1309 | 0.6465 |
589
+ | 0.2879 | 2510 | - | 0.6455 |
590
+ | 0.2891 | 2520 | - | 0.6447 |
591
+ | 0.2902 | 2530 | - | 0.6437 |
592
+ | 0.2914 | 2540 | - | 0.6428 |
593
+ | 0.2925 | 2550 | - | 0.6415 |
594
+ | 0.2937 | 2560 | - | 0.6403 |
595
+ | 0.2948 | 2570 | - | 0.6392 |
596
+ | 0.2960 | 2580 | - | 0.6381 |
597
+ | 0.2971 | 2590 | - | 0.6371 |
598
+ | 0.2983 | 2600 | 1.1006 | 0.6358 |
599
+ | 0.2994 | 2610 | - | 0.6348 |
600
+ | 0.3006 | 2620 | - | 0.6340 |
601
+ | 0.3017 | 2630 | - | 0.6330 |
602
+ | 0.3029 | 2640 | - | 0.6319 |
603
+ | 0.3040 | 2650 | - | 0.6308 |
604
+ | 0.3052 | 2660 | - | 0.6300 |
605
+ | 0.3063 | 2670 | - | 0.6291 |
606
+ | 0.3074 | 2680 | - | 0.6280 |
607
+ | 0.3086 | 2690 | - | 0.6268 |
608
+ | 0.3097 | 2700 | 1.0772 | 0.6254 |
609
+ | 0.3109 | 2710 | - | 0.6243 |
610
+ | 0.3120 | 2720 | - | 0.6232 |
611
+ | 0.3132 | 2730 | - | 0.6224 |
612
+ | 0.3143 | 2740 | - | 0.6215 |
613
+ | 0.3155 | 2750 | - | 0.6205 |
614
+ | 0.3166 | 2760 | - | 0.6194 |
615
+ | 0.3178 | 2770 | - | 0.6183 |
616
+ | 0.3189 | 2780 | - | 0.6171 |
617
+ | 0.3201 | 2790 | - | 0.6160 |
618
+ | 0.3212 | 2800 | 1.0648 | 0.6153 |
619
+ | 0.3224 | 2810 | - | 0.6141 |
620
+ | 0.3235 | 2820 | - | 0.6129 |
621
+ | 0.3247 | 2830 | - | 0.6119 |
622
+ | 0.3258 | 2840 | - | 0.6109 |
623
+ | 0.3269 | 2850 | - | 0.6099 |
624
+ | 0.3281 | 2860 | - | 0.6088 |
625
+ | 0.3292 | 2870 | - | 0.6079 |
626
+ | 0.3304 | 2880 | - | 0.6073 |
627
+ | 0.3315 | 2890 | - | 0.6063 |
628
+ | 0.3327 | 2900 | 1.0398 | 0.6054 |
629
+ | 0.3338 | 2910 | - | 0.6044 |
630
+ | 0.3350 | 2920 | - | 0.6033 |
631
+ | 0.3361 | 2930 | - | 0.6022 |
632
+ | 0.3373 | 2940 | - | 0.6012 |
633
+ | 0.3384 | 2950 | - | 0.6003 |
634
+ | 0.3396 | 2960 | - | 0.5993 |
635
+ | 0.3407 | 2970 | - | 0.5986 |
636
+ | 0.3419 | 2980 | - | 0.5978 |
637
+ | 0.3430 | 2990 | - | 0.5967 |
638
+ | 0.3442 | 3000 | 1.0256 | 0.5959 |
639
+ | 0.3453 | 3010 | - | 0.5947 |
640
+ | 0.3464 | 3020 | - | 0.5937 |
641
+ | 0.3476 | 3030 | - | 0.5929 |
642
+ | 0.3487 | 3040 | - | 0.5920 |
643
+ | 0.3499 | 3050 | - | 0.5908 |
644
+ | 0.3510 | 3060 | - | 0.5897 |
645
+ | 0.3522 | 3070 | - | 0.5888 |
646
+ | 0.3533 | 3080 | - | 0.5882 |
647
+ | 0.3545 | 3090 | - | 0.5874 |
648
+ | 0.3556 | 3100 | 1.0489 | 0.5868 |
649
+ | 0.3568 | 3110 | - | 0.5860 |
650
+ | 0.3579 | 3120 | - | 0.5854 |
651
+ | 0.3591 | 3130 | - | 0.5839 |
652
+ | 0.3602 | 3140 | - | 0.5830 |
653
+ | 0.3614 | 3150 | - | 0.5822 |
654
+ | 0.3625 | 3160 | - | 0.5814 |
655
+ | 0.3637 | 3170 | - | 0.5808 |
656
+ | 0.3648 | 3180 | - | 0.5802 |
657
+ | 0.3660 | 3190 | - | 0.5794 |
658
+ | 0.3671 | 3200 | 1.038 | 0.5788 |
659
+ | 0.3682 | 3210 | - | 0.5778 |
660
+ | 0.3694 | 3220 | - | 0.5770 |
661
+ | 0.3705 | 3230 | - | 0.5763 |
662
+ | 0.3717 | 3240 | - | 0.5752 |
663
+ | 0.3728 | 3250 | - | 0.5745 |
664
+ | 0.3740 | 3260 | - | 0.5737 |
665
+ | 0.3751 | 3270 | - | 0.5728 |
666
+ | 0.3763 | 3280 | - | 0.5720 |
667
+ | 0.3774 | 3290 | - | 0.5713 |
668
+ | 0.3786 | 3300 | 1.0058 | 0.5707 |
669
+ | 0.3797 | 3310 | - | 0.5700 |
670
+ | 0.3809 | 3320 | - | 0.5690 |
671
+ | 0.3820 | 3330 | - | 0.5681 |
672
+ | 0.3832 | 3340 | - | 0.5673 |
673
+ | 0.3843 | 3350 | - | 0.5669 |
674
+ | 0.3855 | 3360 | - | 0.5667 |
675
+ | 0.3866 | 3370 | - | 0.5665 |
676
+ | 0.3877 | 3380 | - | 0.5659 |
677
+ | 0.3889 | 3390 | - | 0.5650 |
678
+ | 0.3900 | 3400 | 1.0413 | 0.5645 |
679
+ | 0.3912 | 3410 | - | 0.5641 |
680
+ | 0.3923 | 3420 | - | 0.5635 |
681
+ | 0.3935 | 3430 | - | 0.5629 |
682
+ | 0.3946 | 3440 | - | 0.5622 |
683
+ | 0.3958 | 3450 | - | 0.5617 |
684
+ | 0.3969 | 3460 | - | 0.5614 |
685
+ | 0.3981 | 3470 | - | 0.5607 |
686
+ | 0.3992 | 3480 | - | 0.5603 |
687
+ | 0.4004 | 3490 | - | 0.5598 |
688
+ | 0.4015 | 3500 | 0.938 | 0.5596 |
689
+ | 0.4027 | 3510 | - | 0.5589 |
690
+ | 0.4038 | 3520 | - | 0.5581 |
691
+ | 0.4050 | 3530 | - | 0.5571 |
692
+ | 0.4061 | 3540 | - | 0.5563 |
693
+ | 0.4073 | 3550 | - | 0.5557 |
694
+ | 0.4084 | 3560 | - | 0.5551 |
695
+ | 0.4095 | 3570 | - | 0.5546 |
696
+ | 0.4107 | 3580 | - | 0.5541 |
697
+ | 0.4118 | 3590 | - | 0.5535 |
698
+ | 0.4130 | 3600 | 0.955 | 0.5528 |
699
+ | 0.4141 | 3610 | - | 0.5522 |
700
+ | 0.4153 | 3620 | - | 0.5516 |
701
+ | 0.4164 | 3630 | - | 0.5509 |
702
+ | 0.4176 | 3640 | - | 0.5503 |
703
+ | 0.4187 | 3650 | - | 0.5495 |
704
+ | 0.4199 | 3660 | - | 0.5490 |
705
+ | 0.4210 | 3670 | - | 0.5481 |
706
+ | 0.4222 | 3680 | - | 0.5475 |
707
+ | 0.4233 | 3690 | - | 0.5467 |
708
+ | 0.4245 | 3700 | 0.9387 | 0.5463 |
709
+ | 0.4256 | 3710 | - | 0.5459 |
710
+ | 0.4268 | 3720 | - | 0.5452 |
711
+ | 0.4279 | 3730 | - | 0.5448 |
712
+ | 0.4290 | 3740 | - | 0.5443 |
713
+ | 0.4302 | 3750 | - | 0.5440 |
714
+ | 0.4313 | 3760 | - | 0.5435 |
715
+ | 0.4325 | 3770 | - | 0.5430 |
716
+ | 0.4336 | 3780 | - | 0.5423 |
717
+ | 0.4348 | 3790 | - | 0.5418 |
718
+ | 0.4359 | 3800 | 0.9672 | 0.5415 |
719
+ | 0.4371 | 3810 | - | 0.5413 |
720
+ | 0.4382 | 3820 | - | 0.5410 |
721
+ | 0.4394 | 3830 | - | 0.5406 |
722
+ | 0.4405 | 3840 | - | 0.5403 |
723
+ | 0.4417 | 3850 | - | 0.5397 |
724
+ | 0.4428 | 3860 | - | 0.5394 |
725
+ | 0.4440 | 3870 | - | 0.5386 |
726
+ | 0.4451 | 3880 | - | 0.5378 |
727
+ | 0.4463 | 3890 | - | 0.5370 |
728
+ | 0.4474 | 3900 | 0.926 | 0.5360 |
729
+ | 0.4485 | 3910 | - | 0.5351 |
730
+ | 0.4497 | 3920 | - | 0.5346 |
731
+ | 0.4508 | 3930 | - | 0.5343 |
732
+ | 0.4520 | 3940 | - | 0.5339 |
733
+ | 0.4531 | 3950 | - | 0.5337 |
734
+ | 0.4543 | 3960 | - | 0.5334 |
735
+ | 0.4554 | 3970 | - | 0.5330 |
736
+ | 0.4566 | 3980 | - | 0.5327 |
737
+ | 0.4577 | 3990 | - | 0.5324 |
738
+ | 0.4589 | 4000 | 0.867 | 0.5319 |
739
+ | 0.4600 | 4010 | - | 0.5313 |
740
+ | 0.4612 | 4020 | - | 0.5308 |
741
+ | 0.4623 | 4030 | - | 0.5300 |
742
+ | 0.4635 | 4040 | - | 0.5293 |
743
+ | 0.4646 | 4050 | - | 0.5287 |
744
+ | 0.4658 | 4060 | - | 0.5284 |
745
+ | 0.4669 | 4070 | - | 0.5281 |
746
+ | 0.4681 | 4080 | - | 0.5277 |
747
+ | 0.4692 | 4090 | - | 0.5272 |
748
+ | 0.4703 | 4100 | 0.916 | 0.5267 |
749
+ | 0.4715 | 4110 | - | 0.5260 |
750
+ | 0.4726 | 4120 | - | 0.5252 |
751
+ | 0.4738 | 4130 | - | 0.5246 |
752
+ | 0.4749 | 4140 | - | 0.5239 |
753
+ | 0.4761 | 4150 | - | 0.5232 |
754
+ | 0.4772 | 4160 | - | 0.5225 |
755
+ | 0.4784 | 4170 | - | 0.5221 |
756
+ | 0.4795 | 4180 | - | 0.5216 |
757
+ | 0.4807 | 4190 | - | 0.5211 |
758
+ | 0.4818 | 4200 | 0.9667 | 0.5206 |
759
+ | 0.4830 | 4210 | - | 0.5204 |
760
+ | 0.4841 | 4220 | - | 0.5200 |
761
+ | 0.4853 | 4230 | - | 0.5192 |
762
+ | 0.4864 | 4240 | - | 0.5187 |
763
+ | 0.4876 | 4250 | - | 0.5185 |
764
+ | 0.4887 | 4260 | - | 0.5179 |
765
+ | 0.4898 | 4270 | - | 0.5173 |
766
+ | 0.4910 | 4280 | - | 0.5170 |
767
+ | 0.4921 | 4290 | - | 0.5165 |
768
+ | 0.4933 | 4300 | 0.9276 | 0.5160 |
769
+ | 0.4944 | 4310 | - | 0.5154 |
770
+ | 0.4956 | 4320 | - | 0.5150 |
771
+ | 0.4967 | 4330 | - | 0.5144 |
772
+ | 0.4979 | 4340 | - | 0.5141 |
773
+ | 0.4990 | 4350 | - | 0.5139 |
774
+ | 0.5002 | 4360 | - | 0.5138 |
775
+ | 0.5013 | 4370 | - | 0.5136 |
776
+ | 0.5025 | 4380 | - | 0.5133 |
777
+ | 0.5036 | 4390 | - | 0.5129 |
778
+ | 0.5048 | 4400 | 0.9331 | 0.5126 |
779
+ | 0.5059 | 4410 | - | 0.5123 |
780
+ | 0.5071 | 4420 | - | 0.5117 |
781
+ | 0.5082 | 4430 | - | 0.5113 |
782
+ | 0.5093 | 4440 | - | 0.5108 |
783
+ | 0.5105 | 4450 | - | 0.5106 |
784
+ | 0.5116 | 4460 | - | 0.5106 |
785
+ | 0.5128 | 4470 | - | 0.5106 |
786
+ | 0.5139 | 4480 | - | 0.5104 |
787
+ | 0.5151 | 4490 | - | 0.5102 |
788
+ | 0.5162 | 4500 | 0.907 | 0.5097 |
789
+ | 0.5174 | 4510 | - | 0.5092 |
790
+ | 0.5185 | 4520 | - | 0.5086 |
791
+ | 0.5197 | 4530 | - | 0.5082 |
792
+ | 0.5208 | 4540 | - | 0.5079 |
793
+ | 0.5220 | 4550 | - | 0.5075 |
794
+ | 0.5231 | 4560 | - | 0.5071 |
795
+ | 0.5243 | 4570 | - | 0.5067 |
796
+ | 0.5254 | 4580 | - | 0.5066 |
797
+ | 0.5266 | 4590 | - | 0.5062 |
798
+ | 0.5277 | 4600 | 0.913 | 0.5059 |
799
+ | 0.5289 | 4610 | - | 0.5056 |
800
+ | 0.5300 | 4620 | - | 0.5052 |
801
+ | 0.5311 | 4630 | - | 0.5046 |
802
+ | 0.5323 | 4640 | - | 0.5039 |
803
+ | 0.5334 | 4650 | - | 0.5033 |
804
+ | 0.5346 | 4660 | - | 0.5030 |
805
+ | 0.5357 | 4670 | - | 0.5028 |
806
+ | 0.5369 | 4680 | - | 0.5027 |
807
+ | 0.5380 | 4690 | - | 0.5023 |
808
+ | 0.5392 | 4700 | 0.9047 | 0.5020 |
809
+ | 0.5403 | 4710 | - | 0.5018 |
810
+ | 0.5415 | 4720 | - | 0.5015 |
811
+ | 0.5426 | 4730 | - | 0.5009 |
812
+ | 0.5438 | 4740 | - | 0.5003 |
813
+ | 0.5449 | 4750 | - | 0.4997 |
814
+ | 0.5461 | 4760 | - | 0.4991 |
815
+ | 0.5472 | 4770 | - | 0.4984 |
816
+ | 0.5484 | 4780 | - | 0.4980 |
817
+ | 0.5495 | 4790 | - | 0.4980 |
818
+ | 0.5506 | 4800 | 0.887 | 0.4979 |
819
+ | 0.5518 | 4810 | - | 0.4975 |
820
+ | 0.5529 | 4820 | - | 0.4973 |
821
+ | 0.5541 | 4830 | - | 0.4969 |
822
+ | 0.5552 | 4840 | - | 0.4966 |
823
+ | 0.5564 | 4850 | - | 0.4964 |
824
+ | 0.5575 | 4860 | - | 0.4964 |
825
+ | 0.5587 | 4870 | - | 0.4960 |
826
+ | 0.5598 | 4880 | - | 0.4957 |
827
+ | 0.5610 | 4890 | - | 0.4955 |
828
+ | 0.5621 | 4900 | 0.8645 | 0.4952 |
829
+ | 0.5633 | 4910 | - | 0.4950 |
830
+ | 0.5644 | 4920 | - | 0.4952 |
831
+ | 0.5656 | 4930 | - | 0.4949 |
832
+ | 0.5667 | 4940 | - | 0.4943 |
833
+ | 0.5679 | 4950 | - | 0.4938 |
834
+ | 0.5690 | 4960 | - | 0.4936 |
835
+ | 0.5702 | 4970 | - | 0.4933 |
836
+ | 0.5713 | 4980 | - | 0.4931 |
837
+ | 0.5724 | 4990 | - | 0.4929 |
838
+ | 0.5736 | 5000 | 0.8348 | 0.4924 |
839
+ | 0.5747 | 5010 | - | 0.4921 |
840
+ | 0.5759 | 5020 | - | 0.4915 |
841
+ | 0.5770 | 5030 | - | 0.4911 |
842
+ | 0.5782 | 5040 | - | 0.4909 |
843
+ | 0.5793 | 5050 | - | 0.4905 |
844
+ | 0.5805 | 5060 | - | 0.4900 |
845
+ | 0.5816 | 5070 | - | 0.4892 |
846
+ | 0.5828 | 5080 | - | 0.4886 |
847
+ | 0.5839 | 5090 | - | 0.4883 |
848
+ | 0.5851 | 5100 | 0.871 | 0.4879 |
849
+ | 0.5862 | 5110 | - | 0.4877 |
850
+ | 0.5874 | 5120 | - | 0.4874 |
851
+ | 0.5885 | 5130 | - | 0.4870 |
852
+ | 0.5897 | 5140 | - | 0.4867 |
853
+ | 0.5908 | 5150 | - | 0.4864 |
854
+ | 0.5919 | 5160 | - | 0.4862 |
855
+ | 0.5931 | 5170 | - | 0.4860 |
856
+ | 0.5942 | 5180 | - | 0.4857 |
857
+ | 0.5954 | 5190 | - | 0.4855 |
858
+ | 0.5965 | 5200 | 0.8522 | 0.4850 |
859
+ | 0.5977 | 5210 | - | 0.4846 |
860
+ | 0.5988 | 5220 | - | 0.4844 |
861
+ | 0.6000 | 5230 | - | 0.4842 |
862
+ | 0.6011 | 5240 | - | 0.4837 |
863
+ | 0.6023 | 5250 | - | 0.4835 |
864
+ | 0.6034 | 5260 | - | 0.4831 |
865
+ | 0.6046 | 5270 | - | 0.4826 |
866
+ | 0.6057 | 5280 | - | 0.4822 |
867
+ | 0.6069 | 5290 | - | 0.4822 |
868
+ | 0.6080 | 5300 | 0.869 | 0.4820 |
869
+ | 0.6092 | 5310 | - | 0.4818 |
870
+ | 0.6103 | 5320 | - | 0.4819 |
871
+ | 0.6114 | 5330 | - | 0.4819 |
872
+ | 0.6126 | 5340 | - | 0.4815 |
873
+ | 0.6137 | 5350 | - | 0.4813 |
874
+ | 0.6149 | 5360 | - | 0.4812 |
875
+ | 0.6160 | 5370 | - | 0.4810 |
876
+ | 0.6172 | 5380 | - | 0.4809 |
877
+ | 0.6183 | 5390 | - | 0.4806 |
878
+ | 0.6195 | 5400 | 0.8548 | 0.4805 |
879
+ | 0.6206 | 5410 | - | 0.4800 |
880
+ | 0.6218 | 5420 | - | 0.4798 |
881
+ | 0.6229 | 5430 | - | 0.4795 |
882
+ | 0.6241 | 5440 | - | 0.4792 |
883
+ | 0.6252 | 5450 | - | 0.4790 |
884
+ | 0.6264 | 5460 | - | 0.4790 |
885
+ | 0.6275 | 5470 | - | 0.4791 |
886
+ | 0.6287 | 5480 | - | 0.4794 |
887
+ | 0.6298 | 5490 | - | 0.4792 |
888
+ | 0.6310 | 5500 | 0.8366 | 0.4790 |
889
+ | 0.6321 | 5510 | - | 0.4786 |
890
+ | 0.6332 | 5520 | - | 0.4780 |
891
+ | 0.6344 | 5530 | - | 0.4773 |
892
+ | 0.6355 | 5540 | - | 0.4768 |
893
+ | 0.6367 | 5550 | - | 0.4767 |
894
+ | 0.6378 | 5560 | - | 0.4765 |
895
+ | 0.6390 | 5570 | - | 0.4765 |
896
+ | 0.6401 | 5580 | - | 0.4763 |
897
+ | 0.6413 | 5590 | - | 0.4760 |
898
+ | 0.6424 | 5600 | 0.8696 | 0.4757 |
899
+ | 0.6436 | 5610 | - | 0.4754 |
900
+ | 0.6447 | 5620 | - | 0.4752 |
901
+ | 0.6459 | 5630 | - | 0.4751 |
902
+ | 0.6470 | 5640 | - | 0.4747 |
903
+ | 0.6482 | 5650 | - | 0.4747 |
904
+ | 0.6493 | 5660 | - | 0.4742 |
905
+ | 0.6505 | 5670 | - | 0.4740 |
906
+ | 0.6516 | 5680 | - | 0.4736 |
907
+ | 0.6527 | 5690 | - | 0.4730 |
908
+ | 0.6539 | 5700 | 0.8302 | 0.4725 |
909
+ | 0.6550 | 5710 | - | 0.4723 |
910
+ | 0.6562 | 5720 | - | 0.4720 |
911
+ | 0.6573 | 5730 | - | 0.4718 |
912
+ | 0.6585 | 5740 | - | 0.4715 |
913
+ | 0.6596 | 5750 | - | 0.4714 |
914
+ | 0.6608 | 5760 | - | 0.4711 |
915
+ | 0.6619 | 5770 | - | 0.4707 |
916
+ | 0.6631 | 5780 | - | 0.4707 |
917
+ | 0.6642 | 5790 | - | 0.4703 |
918
+ | 0.6654 | 5800 | 0.8128 | 0.4703 |
919
+ | 0.6665 | 5810 | - | 0.4701 |
920
+ | 0.6677 | 5820 | - | 0.4699 |
921
+ | 0.6688 | 5830 | - | 0.4697 |
922
+ | 0.6700 | 5840 | - | 0.4698 |
923
+ | 0.6711 | 5850 | - | 0.4695 |
924
+ | 0.6722 | 5860 | - | 0.4691 |
925
+ | 0.6734 | 5870 | - | 0.4689 |
926
+ | 0.6745 | 5880 | - | 0.4689 |
927
+ | 0.6757 | 5890 | - | 0.4688 |
928
+ | 0.6768 | 5900 | 0.8437 | 0.4683 |
929
+ | 0.6780 | 5910 | - | 0.4683 |
930
+ | 0.6791 | 5920 | - | 0.4681 |
931
+ | 0.6803 | 5930 | - | 0.4678 |
932
+ | 0.6814 | 5940 | - | 0.4677 |
933
+ | 0.6826 | 5950 | - | 0.4676 |
934
+ | 0.6837 | 5960 | - | 0.4673 |
935
+ | 0.6849 | 5970 | - | 0.4668 |
936
+ | 0.6860 | 5980 | - | 0.4667 |
937
+ | 0.6872 | 5990 | - | 0.4661 |
938
+ | 0.6883 | 6000 | 0.7774 | 0.4657 |
939
+ | 0.6895 | 6010 | - | 0.4654 |
940
+ | 0.6906 | 6020 | - | 0.4650 |
941
+ | 0.6918 | 6030 | - | 0.4648 |
942
+ | 0.6929 | 6040 | - | 0.4646 |
943
+ | 0.6940 | 6050 | - | 0.4644 |
944
+ | 0.6952 | 6060 | - | 0.4643 |
945
+ | 0.6963 | 6070 | - | 0.4641 |
946
+ | 0.6975 | 6080 | - | 0.4640 |
947
+ | 0.6986 | 6090 | - | 0.4638 |
948
+ | 0.6998 | 6100 | 0.834 | 0.4637 |
949
+ | 0.7009 | 6110 | - | 0.4633 |
950
+ | 0.7021 | 6120 | - | 0.4632 |
951
+ | 0.7032 | 6130 | - | 0.4631 |
952
+ | 0.7044 | 6140 | - | 0.4628 |
953
+ | 0.7055 | 6150 | - | 0.4627 |
954
+ | 0.7067 | 6160 | - | 0.4623 |
955
+ | 0.7078 | 6170 | - | 0.4617 |
956
+ | 0.7090 | 6180 | - | 0.4615 |
957
+ | 0.7101 | 6190 | - | 0.4614 |
958
+ | 0.7113 | 6200 | 0.8118 | 0.4612 |
959
+ | 0.7124 | 6210 | - | 0.4612 |
960
+ | 0.7135 | 6220 | - | 0.4612 |
961
+ | 0.7147 | 6230 | - | 0.4610 |
962
+ | 0.7158 | 6240 | - | 0.4609 |
963
+ | 0.7170 | 6250 | - | 0.4610 |
964
+ | 0.7181 | 6260 | - | 0.4611 |
965
+ | 0.7193 | 6270 | - | 0.4607 |
966
+ | 0.7204 | 6280 | - | 0.4599 |
967
+ | 0.7216 | 6290 | - | 0.4598 |
968
+ | 0.7227 | 6300 | 0.7884 | 0.4600 |
969
+ | 0.7239 | 6310 | - | 0.4599 |
970
+ | 0.7250 | 6320 | - | 0.4600 |
971
+ | 0.7262 | 6330 | - | 0.4601 |
972
+ | 0.7273 | 6340 | - | 0.4603 |
973
+ | 0.7285 | 6350 | - | 0.4603 |
974
+ | 0.7296 | 6360 | - | 0.4598 |
975
+ | 0.7308 | 6370 | - | 0.4597 |
976
+ | 0.7319 | 6380 | - | 0.4596 |
977
+ | 0.7331 | 6390 | - | 0.4594 |
978
+ | 0.7342 | 6400 | 0.8092 | 0.4590 |
979
+ | 0.7353 | 6410 | - | 0.4588 |
980
+ | 0.7365 | 6420 | - | 0.4585 |
981
+ | 0.7376 | 6430 | - | 0.4584 |
982
+ | 0.7388 | 6440 | - | 0.4580 |
983
+ | 0.7399 | 6450 | - | 0.4574 |
984
+ | 0.7411 | 6460 | - | 0.4570 |
985
+ | 0.7422 | 6470 | - | 0.4566 |
986
+ | 0.7434 | 6480 | - | 0.4563 |
987
+ | 0.7445 | 6490 | - | 0.4560 |
988
+ | 0.7457 | 6500 | 0.8195 | 0.4557 |
989
+ | 0.7468 | 6510 | - | 0.4556 |
990
+ | 0.7480 | 6520 | - | 0.4554 |
991
+ | 0.7491 | 6530 | - | 0.4551 |
992
+ | 0.7503 | 6540 | - | 0.4548 |
993
+ | 0.7514 | 6550 | - | 0.4545 |
994
+ | 0.7526 | 6560 | - | 0.4543 |
995
+ | 0.7537 | 6570 | - | 0.4541 |
996
+ | 0.7548 | 6580 | - | 0.4540 |
997
+ | 0.7560 | 6590 | - | 0.4538 |
998
+ | 0.7571 | 6600 | 0.8163 | 0.4535 |
999
+ | 0.7583 | 6610 | - | 0.4533 |
1000
+ | 0.7594 | 6620 | - | 0.4536 |
1001
+ | 0.7606 | 6630 | - | 0.4535 |
1002
+ | 0.7617 | 6640 | - | 0.4533 |
1003
+ | 0.7629 | 6650 | - | 0.4532 |
1004
+ | 0.7640 | 6660 | - | 0.4531 |
1005
+ | 0.7652 | 6670 | - | 0.4531 |
1006
+ | 0.7663 | 6680 | - | 0.4530 |
1007
+ | 0.7675 | 6690 | - | 0.4528 |
1008
+ | 0.7686 | 6700 | 0.8091 | 0.4527 |
1009
+ | 0.7698 | 6710 | - | 0.4527 |
1010
+ | 0.7709 | 6720 | - | 0.4526 |
1011
+ | 0.7721 | 6730 | - | 0.4525 |
1012
+ | 0.7732 | 6740 | - | 0.4524 |
1013
+ | 0.7743 | 6750 | - | 0.4521 |
1014
+ | 0.7755 | 6760 | - | 0.4517 |
1015
+ | 0.7766 | 6770 | - | 0.4514 |
1016
+ | 0.7778 | 6780 | - | 0.4512 |
1017
+ | 0.7789 | 6790 | - | 0.4514 |
1018
+ | 0.7801 | 6800 | 0.8098 | 0.4515 |
1019
+ | 0.7812 | 6810 | - | 0.4514 |
1020
+ | 0.7824 | 6820 | - | 0.4511 |
1021
+ | 0.7835 | 6830 | - | 0.4507 |
1022
+ | 0.7847 | 6840 | - | 0.4505 |
1023
+ | 0.7858 | 6850 | - | 0.4504 |
1024
+ | 0.7870 | 6860 | - | 0.4503 |
1025
+ | 0.7881 | 6870 | - | 0.4500 |
1026
+ | 0.7893 | 6880 | - | 0.4498 |
1027
+ | 0.7904 | 6890 | - | 0.4495 |
1028
+ | 0.7916 | 6900 | 0.7857 | 0.4491 |
1029
+ | 0.7927 | 6910 | - | 0.4490 |
1030
+ | 0.7939 | 6920 | - | 0.4488 |
1031
+ | 0.7950 | 6930 | - | 0.4488 |
1032
+ | 0.7961 | 6940 | - | 0.4488 |
1033
+ | 0.7973 | 6950 | - | 0.4487 |
1034
+ | 0.7984 | 6960 | - | 0.4484 |
1035
+ | 0.7996 | 6970 | - | 0.4482 |
1036
+ | 0.8007 | 6980 | - | 0.4483 |
1037
+ | 0.8019 | 6990 | - | 0.4481 |
1038
+ | 0.8030 | 7000 | 0.7817 | 0.4477 |
1039
+ | 0.8042 | 7010 | - | 0.4476 |
1040
+ | 0.8053 | 7020 | - | 0.4471 |
1041
+ | 0.8065 | 7030 | - | 0.4469 |
1042
+ | 0.8076 | 7040 | - | 0.4468 |
1043
+ | 0.8088 | 7050 | - | 0.4465 |
1044
+ | 0.8099 | 7060 | - | 0.4460 |
1045
+ | 0.8111 | 7070 | - | 0.4458 |
1046
+ | 0.8122 | 7080 | - | 0.4458 |
1047
+ | 0.8134 | 7090 | - | 0.4454 |
1048
+ | 0.8145 | 7100 | 0.779 | 0.4452 |
1049
+ | 0.8156 | 7110 | - | 0.4449 |
1050
+ | 0.8168 | 7120 | - | 0.4448 |
1051
+ | 0.8179 | 7130 | - | 0.4446 |
1052
+ | 0.8191 | 7140 | - | 0.4442 |
1053
+ | 0.8202 | 7150 | - | 0.4442 |
1054
+ | 0.8214 | 7160 | - | 0.4441 |
1055
+ | 0.8225 | 7170 | - | 0.4440 |
1056
+ | 0.8237 | 7180 | - | 0.4437 |
1057
+ | 0.8248 | 7190 | - | 0.4434 |
1058
+ | 0.8260 | 7200 | 0.7807 | 0.4434 |
1059
+ | 0.8271 | 7210 | - | 0.4435 |
1060
+ | 0.8283 | 7220 | - | 0.4433 |
1061
+ | 0.8294 | 7230 | - | 0.4431 |
1062
+ | 0.8306 | 7240 | - | 0.4430 |
1063
+ | 0.8317 | 7250 | - | 0.4428 |
1064
+ | 0.8329 | 7260 | - | 0.4426 |
1065
+ | 0.8340 | 7270 | - | 0.4424 |
1066
+ | 0.8351 | 7280 | - | 0.4428 |
1067
+ | 0.8363 | 7290 | - | 0.4426 |
1068
+ | 0.8374 | 7300 | 0.7724 | 0.4423 |
1069
+ | 0.8386 | 7310 | - | 0.4419 |
1070
+ | 0.8397 | 7320 | - | 0.4418 |
1071
+ | 0.8409 | 7330 | - | 0.4417 |
1072
+ | 0.8420 | 7340 | - | 0.4415 |
1073
+ | 0.8432 | 7350 | - | 0.4413 |
1074
+ | 0.8443 | 7360 | - | 0.4409 |
1075
+ | 0.8455 | 7370 | - | 0.4406 |
1076
+ | 0.8466 | 7380 | - | 0.4405 |
1077
+ | 0.8478 | 7390 | - | 0.4400 |
1078
+ | 0.8489 | 7400 | 0.7898 | 0.4393 |
1079
+ | 0.8501 | 7410 | - | 0.4389 |
1080
+ | 0.8512 | 7420 | - | 0.4384 |
1081
+ | 0.8524 | 7430 | - | 0.4381 |
1082
+ | 0.8535 | 7440 | - | 0.4380 |
1083
+ | 0.8547 | 7450 | - | 0.4380 |
1084
+ | 0.8558 | 7460 | - | 0.4379 |
1085
+ | 0.8569 | 7470 | - | 0.4377 |
1086
+ | 0.8581 | 7480 | - | 0.4377 |
1087
+ | 0.8592 | 7490 | - | 0.4376 |
1088
+ | 0.8604 | 7500 | 0.8009 | 0.4375 |
1089
+ | 0.8615 | 7510 | - | 0.4371 |
1090
+ | 0.8627 | 7520 | - | 0.4369 |
1091
+ | 0.8638 | 7530 | - | 0.4365 |
1092
+ | 0.8650 | 7540 | - | 0.4362 |
1093
+ | 0.8661 | 7550 | - | 0.4359 |
1094
+ | 0.8673 | 7560 | - | 0.4357 |
1095
+ | 0.8684 | 7570 | - | 0.4355 |
1096
+ | 0.8696 | 7580 | - | 0.4351 |
1097
+ | 0.8707 | 7590 | - | 0.4347 |
1098
+ | 0.8719 | 7600 | 0.7847 | 0.4346 |
1099
+ | 0.8730 | 7610 | - | 0.4346 |
1100
+ | 0.8742 | 7620 | - | 0.4344 |
1101
+ | 0.8753 | 7630 | - | 0.4343 |
1102
+ | 0.8764 | 7640 | - | 0.4338 |
1103
+ | 0.8776 | 7650 | - | 0.4336 |
1104
+ | 0.8787 | 7660 | - | 0.4332 |
1105
+ | 0.8799 | 7670 | - | 0.4331 |
1106
+ | 0.8810 | 7680 | - | 0.4329 |
1107
+ | 0.8822 | 7690 | - | 0.4326 |
1108
+ | 0.8833 | 7700 | 0.7668 | 0.4324 |
1109
+ | 0.8845 | 7710 | - | 0.4325 |
1110
+ | 0.8856 | 7720 | - | 0.4327 |
1111
+ | 0.8868 | 7730 | - | 0.4329 |
1112
+ | 0.8879 | 7740 | - | 0.4328 |
1113
+ | 0.8891 | 7750 | - | 0.4325 |
1114
+ | 0.8902 | 7760 | - | 0.4325 |
1115
+ | 0.8914 | 7770 | - | 0.4326 |
1116
+ | 0.8925 | 7780 | - | 0.4324 |
1117
+ | 0.8937 | 7790 | - | 0.4322 |
1118
+ | 0.8948 | 7800 | 0.7987 | 0.4320 |
1119
+ | 0.8960 | 7810 | - | 0.4319 |
1120
+ | 0.8971 | 7820 | - | 0.4318 |
1121
+ | 0.8982 | 7830 | - | 0.4315 |
1122
+ | 0.8994 | 7840 | - | 0.4312 |
1123
+ | 0.9005 | 7850 | - | 0.4308 |
1124
+ | 0.9017 | 7860 | - | 0.4308 |
1125
+ | 0.9028 | 7870 | - | 0.4309 |
1126
+ | 0.9040 | 7880 | - | 0.4306 |
1127
+ | 0.9051 | 7890 | - | 0.4305 |
1128
+ | 0.9063 | 7900 | 0.7691 | 0.4305 |
1129
+ | 0.9074 | 7910 | - | 0.4305 |
1130
+ | 0.9086 | 7920 | - | 0.4308 |
1131
+ | 0.9097 | 7930 | - | 0.4309 |
1132
+ | 0.9109 | 7940 | - | 0.4309 |
1133
+ | 0.9120 | 7950 | - | 0.4305 |
1134
+ | 0.9132 | 7960 | - | 0.4297 |
1135
+ | 0.9143 | 7970 | - | 0.4294 |
1136
+ | 0.9155 | 7980 | - | 0.4292 |
1137
+ | 0.9166 | 7990 | - | 0.4292 |
1138
+ | 0.9177 | 8000 | 0.7828 | 0.4289 |
1139
+
1140
+ </details>
1141
+
1142
+ ### Framework Versions
1143
+ - Python: 3.12.8
1144
+ - Sentence Transformers: 3.4.1
1145
+ - Transformers: 4.49.0
1146
+ - PyTorch: 2.2.0+cu121
1147
+ - Accelerate: 1.4.0
1148
+ - Datasets: 3.3.2
1149
+ - Tokenizers: 0.21.0
1150
+
1151
+ ## Citation
1152
+
1153
+ ### BibTeX
1154
+
1155
+ #### Sentence Transformers
1156
+ ```bibtex
1157
+ @inproceedings{reimers-2019-sentence-bert,
1158
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1159
+ author = "Reimers, Nils and Gurevych, Iryna",
1160
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1161
+ month = "11",
1162
+ year = "2019",
1163
+ publisher = "Association for Computational Linguistics",
1164
+ url = "https://arxiv.org/abs/1908.10084",
1165
+ }
1166
+ ```
1167
+
1168
+ #### MultipleNegativesRankingLoss
1169
+ ```bibtex
1170
+ @misc{henderson2017efficient,
1171
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
1172
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
1173
+ year={2017},
1174
+ eprint={1705.00652},
1175
+ archivePrefix={arXiv},
1176
+ primaryClass={cs.CL}
1177
+ }
1178
+ ```
1179
+
1180
+ <!--
1181
+ ## Glossary
1182
+
1183
+ *Clearly define terms in order to be accessible across audiences.*
1184
+ -->
1185
+
1186
+ <!--
1187
+ ## Model Card Authors
1188
+
1189
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1190
+ -->
1191
+
1192
+ <!--
1193
+ ## Model Card Contact
1194
+
1195
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1196
+ -->
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